The past, present, and future of the Brain Imaging Data Structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS

Imaging Monoaminergic Systems and their Pharmacological Control

The natural sciences always and only enhance the human condition by producing knowledge which empowers us to control the natural world, where otherwise it would control us. Nowhere is the power of uncontrollable natural phenomena to curb or limit human well-being more pervasive than in the human mind itself. Monoamines are a class of neurotransmitters consistently implicated in the etiology of nonvolitional neuropsychological phenomena. They are a cornerstone of those mental functions which humans most desire, but are least able to control. Unsurprisingly, drugs targeting these neurotransmitter systems are widely used in clinical, therapeutic, performance-enhancing, and recreational contexts. To the detriment of patients and users, however, currently available drugs are strongly lacking in terms of effect amplitude, reliability, and persistence, as well as suitability for long-term use. We present novel research, which advances the descriptive understanding of drug-naïve monoaminergic function and of monoaminergic drug effects. Our work includes methods development, the investigation of functional monoaminergic neurophenotypes, and the imaging-based profiling of longitudinal drug treatment. The neurobiological representations we put forward are instrumental to refining the understanding of psychopharmacological intervention profiles and the phenomena which they are able to modulate. Methodologically, we tackle technological impediments to large-scale (longitudinal, multi-cohort, and multi-center) preclinical brain imaging. Our first article deals with the challenge of automatically and reliably preparing preclinical magnetic resonance imaging (MRI) data for sharing and analysis. Our second article deals with the improvement of mouse brain registration and the definition of a reference space. Both of the above, as well as further relevant data analysis, rely heavily on high-level software tools. We consequently make an excursion into neuroscientific software management, which needs to be as accessible, reproducible, and transparent as the research it supports. In our third article we present the first whole-brain read-out of ventral tegmental area (VTA) dopaminergic signalling in the mouse. We perform a multivariate analysis of experiment parameters, and formulate specific guidelines for assay reuse or refinement. In our fourth article we apply a previously established serotonergic activity read-out to a longitudinal selective serotonin reuptake inhibitor (SSRI) drug treatment. We produce the first functional neuroimaging profile of longitudinal serotonergic drug effects, and we identify distinct brain clusters based on longitudinal activation trajectories. Our findings both support the autoinhibition down-regulation theory for the SSRI action mechanism, and complement it, by suggesting a prominent role for brainstem involvement. As all trajectories show significant treatment but no post-treatment effects, we also provide neuroimaging evidence strongly suggesting that the intervention fails to elicit persistent homeostatic shifts in healthy subjects. We openly share all acquired data, and all code required to reproduce our analyses. We suggest that the novel methods and the neurophenotypical profiling concept which we put forward may revitalize psychopharmacological research. Our work ultimately serves to advance the understanding of monoaminergic function and its manipulation, as is needed to fulfill the increasing need for the betterment of the human mind, in and outside of the clinical context

LabbookDB Presentation - A Relational Framework for Laboratory Metadata

LabbookDB is a relational database application framework for life sciences—providing an extendable schema and functions to conveniently add and retrieve information, and generate summaries. The core concept of LabbookDB is that wet work metadata commonly tracked in lab books or spreadsheets is more efficiently and more reliably stored in a relational database, and more flexibly queried. We overcome the flexibility limitations of designed-for-analysis spreadsheets and databases by building our schema around atomized physical object interactions in the laboratory (and providing plotting- and/or analysis-ready dataframes as a compatibility layer). We keep our database schema more easily extendable and adaptable by using joined table inheritance to manage polymorphic objects and their relationships. LabbookDB thus provides a wet work metadata storage model excellently suited for exploratory ex-post reporting and analysis, as well as a potential infrastructure for automated wet work tracking

An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging

The reliability of scientific results critically depends on reproducible and transparent data processing. Cross-subject and cross-study comparability of imaging data in general, and magnetic resonance imaging (MRI) data in particular, is contingent on the quality of registration to a standard reference space. In small animal MRI this is not adequately provided by currently used processing workflows, which utilize high-level scripts optimized for human data, and adapt animal data to fit the scripts, rather than vice-versa. In this fully reproducible article we showcase a generic workflow optimized for the mouse brain, alongside a standard reference space suited to harmonize data between analysis and operation. We present four separate metrics for automated quality control (QC), and a visualization method to aid operator inspection. Benchmarking this workflow against common legacy practices reveals that it performs more consistently, better preserves variance across subjects while minimizing variance across sessions, and improves both volume and smoothness conservation RMSE approximately 3-fold. We propose this open source workflow and the QC metrics as a new standard for small animal MRI registration, ensuring workflow robustness, data comparability, and region assignment validity, important criteria for the comparability of scientific results across experiments and centers

Whole-brain opto-fMRI map of mouse VTA dopaminergic activation reflects structural projections with small but significant deviations

Ascending dopaminergic projections from neurons located in the Ventral Tegmental Area (VTA) are key to the etiology, dysfunction, and control of motivation, learning, and addiction. Due to the evolutionary conservation of this nucleus and the extensive use of mice as disease models, establishing an assay for VTA dopaminergic signaling in the mouse brain is crucial for the translational investigation of motivational control as well as of neuronal function phenotypes for diseases and interventions. In this article we use optogenetic stimulation directed at VTA dopaminergic neurons in combination with functional Magnetic Resonance Imaging (fMRI), a method widely used in human deep brain imaging. We present a comprehensive assay producing the first whole-brain opto-fMRI map of dopaminergic activation in the mouse, and show that VTA dopaminergic system function is consistent with its structural VTA projections, diverging only in a few key aspects. While the activation map predominantly highlights target areas according to their relative projection densities (e.g., strong activation of the nucleus accumbens and low activation of the hippocampus), it also includes areas for which a structural connection is not well established (such as the dorsomedial striatum). We further detail the variability of the assay with regard to multiple experimental parameters, including stimulation protocol and implant position, and provide evidence-based recommendations for assay reuse, publishing both reference results and a reference analysis workflow implementation.ISSN:2158-318

An in vivo wound healing model for the characterization of the angiogenic process and its modulation by pharmacological interventions

Angiogenesis during wound healing is essential for tissue repair and also affected during cancer treatment by anti-angiogenic drugs. Here, we introduce a minimally invasive wound healing model in the mouse ear to assess angiogenesis with high spatiotemporal resolution in a longitudinal manner in vivo using two-photon microscopy in mice expressing GCaMP2 in arterial endothelial cells. The development of vascular sprouts occurred in a highly orchestrated manner within a time window of 8 days following wounding. Novel sprouts developed exclusively from the distal stump of the transsected arteries, growing towards the proximal arterial stump. This was in line with the incidence of Ca2+ transients in the arterial endothelial cells, most probably a result of VEGF stimulation, which were more numerous on the distal part. Functional analysis revealed perfusion across the wound site via arterial sprouts developed between days 6 and 8 following the incision. At day 8, proximal and distal arteries were structurally and functionally connected, though only 2/3 of all sprouts detected were actually perfused. Treatment with the FDA approved drug, sunitinib, the preclinical drug AZD4547, as well as with the combination of the two agents had significant effects on both structural and functional readouts of neo-angiogenesis. The simplicity and high reproducibility of the model makes it an attractive tool for elucidating migratory activity, phenotype and functionality of endothelial cells during angiogenesis and for evaluating specific anti-angiogenic drug interventions.ISSN:2045-232

Contrast Optimized Stimulus generator (COSgen)

<p>As fMRI measurements are complex, expensive, and often target low-amplitude effects, the<br> statistical optimization of stimulus sequences for specific experimental targets plays an integral<br> role in the experiment design process. We provide an extremely flexible framework to perform<br> such optimizations using the genetic algorithm (GA) introduced by Wager and Nichols [6]<br> and improved by Kao et al. [4]. Our Python implementation improves on many aspects of<br> existing solutions, including higher parameterization, API availability, and detailed documen-<br> tation. Because of its modular structure, our implementation is highly adaptable to specific<br> use cases in both human and animal fMRI. Nevertheless, we also provide an easy to use de-<br> fault algorithm, with widely applicable and properly cited presets. The implementation allows<br> full model (design matrix construction and covariance matrix computation) as well as fitness<br> measure specification.</p

An Optimized Registration Workflow and Standard Geometric Space for Small Animal Brain Imaging

The reliability of scientific results critically depends on reproducible and transparent data processing. Cross-subject and cross-study comparability of imaging data in general, and magnetic resonance imaging (MRI) data in particular, is contingent on the quality of registration to a standard reference space. In small animal MRI this is not adequately provided by currently used processing workflows, which utilize high-level scripts optimized for human data, and adapt animal data to fit the scripts, rather than vice-versa. In this fully reproducible article we showcase a generic workflow optimized for the mouse brain, alongside a standard reference space suited to harmonize data between analysis and operation. We present four separate metrics for automated quality control (QC), and a visualization method to aid operator inspection. Benchmarking this workflow against common legacy practices reveals that it performs more consistently, better preserves variance across subjects while minimizing variance across sessions, and improves both volume and smoothness conservation RMSE approximately 3-fold. We propose this open source workflow and the QC metrics as a new standard for small animal MRI registration, ensuring workflow robustness, data comparability, and region assignment validity, important criteria for the comparability of scientific results across experiments and centers

Selective amotivation deficits following chronic psychosocial stress in mice

Amotivation is a major symptom of several psychiatric disorders. However, which specific motivations are most affected in various illnesses is not well understood. In major depressive disorder (MDD), anecdotal evidence suggests the motivation to explore may be especially affected, but direct evidence from either patients or animal models is lacking. To investigate the potential for, and nature of, exploratory drive deficits in MDD, we subjected mice to a chronic social defeat (CSD) manipulation that gives rise to a MDD-like behavioural ensemble, and performed a behavioural battery to examine bodyweight homeostasis, ambulation, anxiety, exploratory behaviour motivated by either novelty or fear, and short-term memory. Consistent with previous reports, we found a disruption of bodyweight homeostasis and reduced ambulation following CSD treatment, but we found no evidence for anxiogenic effects or impairments in short-term memory. Surprisingly, we also observed profoundly delayed and diminished exploration of novel, safe space following CSD, while exploration motivated by fear remained intact. These results extend our knowledge of the behavioural phenotypes in mice resulting from CSD by homing in on specific motivational drives. In MDD patients, reduced exploration could compound disease symptomatology by preventing engagement in what could be rewarding exploration experiences, and targeting deficits in the motivation to explore may represent a novel avenue for treatment

An Automated Open-Source Workflow for Standards-Compliant Integration of Small Animal Magnetic Resonance Imaging Data

Large-scale research integration is contingent on seamless access to data in standardized formats. Standards enable researchers to understand external experiment structures, pool results, and apply homogeneous preprocessing and analysis workflows. Particularly, they facilitate these features without the need for numerous potentially confounding compatibility add-ons. In small animal magnetic resonance imaging, an overwhelming proportion of data is acquired via the ParaVision software of the Bruker Corporation. The original data structure is predominantly transparent, but fundamentally incompatible with modern pipelines. Additionally, it sources metadata from free-field operator input, which diverges strongly between laboratories and researchers. In this article we present an open-source workflow which automatically converts and reposits data from the ParaVision structure into the widely supported and openly documented Brain Imaging Data Structure (BIDS). Complementing this workflow we also present operator guidelines for appropriate ParaVision data input, and a programmatic walk-through detailing how preexisting scans with uninterpretable metadata records can easily be made compliant after the acquisition